The present invention relates to cardiac imaging, and more particularly, to left atrium segmentation in C-arm computed tomography (CT) images.
Strokes are the third leading cause of death in the United States. Approximately fifteen percent of all strokes are caused by atrial fibrillation (AF). As a widely used minimally invasive surgery to treat AF, a catheter based ablation procedure uses high radio-frequency energy to eliminate sources of ectopic foci, especially around the ostia of the appendage and the pulmonary veins (PV). Automatic segmentation of the left atrium (LA) is important for pre-operative assessment to identify the potential sources of electric events. However, there are large variations in PV drainage patterns between different patients. For example, the most common variations, which are found in 20-30% of the population, are extra right PVs and left common PVs (where two left PVs merge into one before joining the chamber).
Conventional LA segmentation methods can be roughly categorized as non-model based or model-based approaches. The non-model based approaches do not assume any prior knowledge of the LA shape and the whole segmentation procedure is purely data driven. An advantage of non-model based methods is that they can handle structural variations of the PVs. However, such methods cannot provide the underlying anatomical information (e.g., which part of the segmentation is the left inferior PV). In practice non-model based approaches work well on computed tomography (CT) or magnetic resonance imaging (MRI) data, but such methods are typically not robust on challenging C-arm CT images. Model based approaches exploit a prior shape of the LA (either in the form of an atlas or a mean shape mesh) to guide the segmentation. Using a prior shape constraint typically allows model based approaches to avoid leakage around weak or missing boundaries, which plagues non-model based approaches. However, using one mean shape, it is difficult to handle structural variations (e.g., the left common PV). In order to address PV variations, multiple atlases are required, which costs extra computation time.